Rezo.AI automates 15 million daily customer calls with agentic AI

How Rezo.AI works

Rezo.AI describes itself as an agentic AI product company that automates customer interactions for large enterprises. Its platform operates across voice, chat, and WhatsApp, integrating with client systems to resolve queries without human intervention. According to YourStory, the AI is designed not just to respond to customers but to understand their needs and execute actions, such as booking service appointments or updating claim statuses.

The startup uses a usage-based pricing model, charging clients based on call volume, AI utilization, and the complexity of tasks handled. This model scales with the client’s needs, making it adaptable for enterprises of varying sizes.

Clients and use cases

Rezo.AI serves clients across sectors like banking, financial services, NBFCs, automotive, consumer durables, energy, and telecom. Notable clients include Maruti Suzuki, Livguard, L&T, and Spinny, according to the source.

For Maruti Suzuki, Rezo.AI’s AI agents handle service bookings, managing approximately 750,000 bookings monthly. In insurance, the platform automates calls related to claim status updates, reducing the need for human agents.

Funding and growth strategy

Unlike many startups that prioritize raising external funding, Rezo.AI has reinvested its revenue into product development and expansion. The company raised around ₹2 crore in external funding in 2021 but has focused on profitability and sustainable growth since then, per YourStory.

Rezo.AI’s co-founder, Dr. Rashi Gupta, emphasized the importance of building a product that works effectively in real-world conditions. The startup spent two years refining its AI to handle challenges like regional languages and inconsistent call quality before launching commercially.

Challenges in scaling agentic AI

Rezo.AI faced hurdles in adapting its AI to India’s diverse linguistic landscape. Regional dialects and varying call quality initially impacted accuracy, requiring real-time data to improve models for different regions, according to YourStory.

Another challenge was gaining enterprise trust. Many companies were skeptical about AI replacing human jobs. Rezo.AI addressed this by starting with small pilots to demonstrate results and positioning its AI as a tool to augment human work rather than replace it.

During peak periods like festivals or large campaigns, the sudden spike in call volumes tested the platform’s scalability. Rezo.AI implemented auto-scaling and continuous monitoring to handle these surges.

Future plans

Rezo.AI is expanding its offerings to include solutions for collections and developing a DIY platform. This platform aims to enable businesses to create production-ready AI agents quickly, reducing reliance on technical teams.

While India remains its primary market, the company has established a presence in Singapore and plans to explore other international markets. However, it continues to prioritize sustainable growth over rapid expansion.

What this means

Lazyfounder analysis — our interpretation, not reported fact.

Rezo.AI’s model showcases how agentic AI can be deployed at scale to handle routine customer service tasks, freeing human agents for more complex queries. For founders and operators, this signals a shift in enterprise adoption of AI beyond chatbots—moving toward systems that can execute actions autonomously. The startup’s focus on profitability over frequent funding rounds also highlights an alternative growth path for AI companies, particularly in markets where operational challenges like language diversity and call quality are significant.

Key takeaways

• Enterprises in customer-heavy sectors can evaluate agentic AI to reduce manual intervention in routine queries like service bookings or claim updates.
• Rezo.AI’s model demonstrates that bootstrapped growth is viable for AI startups if they prioritize product-market fit over early scaling.
• Indian startups building AI for customer service must account for regional language variations and poor call quality, which can impact accuracy.
• The shift from demo-based AI to real-world deployment requires rigorous testing, as Rezo.AI’s two-year refinement period shows.